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[#146] Which one of the following is not a major strength of the neural network approach?

(A) neural network learning algorithms are guaranteed to converge to an optimal solution

(B) neural networks work well with datasets containing noisy data

(C) neural networks can be used for both supervised learning and unsupervised clustering

(D) neural networks can be used for applications that require a time element to be included in the data

(E) neural network learning algorithms are guaranteed to converge to an optimal solution

(F) neural networks work well with datasets containing noisy data

(G) neural networks can be used for both supervised learning and unsupervised clustering

(H) neural networks can be used for applications that require a time element to be included in the data

Correct Answer

(A) neural network learning algorithms are guaranteed to converge to an optimal solution
(E) neural network learning algorithms are guaranteed to converge to an optimal solution

[#147] Given above is a description of a neural network. When does a neural network model become a deep learning model?

(A) when you add more hidden layers and increase depth of neural network

(B) when there is higher dimensionality of data

(C) when the problem is an image recognition problem

(D) when there is lower dimensionality of data

(E) when you add more hidden layers and increase depth of neural network

(F) when there is higher dimensionality of data

(G) when the problem is an image recognition problem

(H) when there is lower dimensionality of data

Correct Answer

(A) when you add more hidden layers and increase depth of neural network
(E) when you add more hidden layers and increase depth of neural network

[#148] Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering? 1. Single-link 2. Complete-link 3. Average-link

(A) 1 and 2

(B) 1 and 3

(C) 2 and 3

(D) 1, 2 and 3

(E) 1 and 2

(F) 1 and 3

(G) 2 and 3

(H) 1, 2 and 3

Correct Answer

(D) 1, 2 and 3
(H) 1, 2 and 3

[#149] which among the following is the most appropriate kernel that can be used with SVM to separate the classes.

(A) linear kernel

(B) gaussian rbf kernel

(C) polynomial kernel

(D) option 1 and option 3

(E) linear kernel

(F) gaussian rbf kernel

(G) polynomial kernel

(H) option 1 and option 3

Correct Answer

(B) gaussian rbf kernel
(F) gaussian rbf kernel

[#150] Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the original responses for data points along the two branches (left & right respectively) and what is the new response after collapsing the node?

(A) 10.8, 13.33, 14.48

(B) 10.8, 13.33, 12.06

(C) 7.2, 10, 8.8

(D) 7.2, 10, 8.6

(E) 10.8, 13.33, 14.48

(F) 10.8, 13.33, 12.06

(G) 7.2, 10, 8.8

(H) 7.2, 10, 8.6